Scanning the Issue

Abstract

<b>Deep Reinforcement Learning for Autonomous Driving: A Survey</b><i>R. Kiran, I. Sobh, V. Talpaert, P. Mannion, A. A. Al Sallab, S. Yogamani, and P. P&#x00E9;rez</i>With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high-dimensional environments. This review summarizes deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in the real-world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, and inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test, and robustify existing solutions in RL are discussed.